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http://dx.doi.org/10.9717/kmms.2018.21.12.1459

Multi-modal Sensor System and Database for Human Detection and Activity Learning of Robot in Outdoor  

Uhm, Taeyoung (Field Robotics R&D Division, Korean Institute of Robot and Convergence)
Park, Jeong-Woo (Field Robotics R&D Division, Korean Institute of Robot and Convergence)
Lee, Jong-Deuk (Field Robotics R&D Division, Korean Institute of Robot and Convergence)
Bae, Gi-Deok (Field Robotics R&D Division, Korean Institute of Robot and Convergence)
Choi, Young-Ho (Field Robotics R&D Division, Korean Institute of Robot and Convergence)
Publication Information
Journal of Korea Multimedia Society / v.21, no.12, 2018 , pp. 1459-1466 More about this Journal
Abstract
Robots which detect human and recognize action are important factors for human interaction, and many researches have been conducted. Recently, deep learning technology has developed and learning based robot's technology is a major research area. These studies require a database to learn and evaluate for intelligent human perception. In this paper, we propose a multi-modal sensor-based image database condition considering the security task by analyzing the image database to detect the person in the outdoor environment and to recognize the behavior during the running of the robot.
Keywords
Robot Database; Human Activity Learning; Outdoor Database; Human Detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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